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To track the 3D locations and trajectories of the other traffic participantsat any given time, modern autonomous vehicles are equipped with multiplecameras that cover the vehicle's full surroundings. Yet, camera-based 3D objecttracking methods prioritize optimizing the single-camera setup and resort topost-hoc fusion in a multi-camera setup. In this paper, we propose a method forpanoramic 3D object tracking, called CC-3DT, that associates and models objecttrajectories both temporally and across views, and improves the overalltracking consistency. In particular, our method fuses 3D detections frommultiple cameras before association, reducing identity switches significantlyand improving motion modeling. Our experiments on large-scale driving datasetsshow that fusion before association leads to a large margin of improvement overpost-hoc fusion. We set a new state-of-the-art with 12.6% improvement inaverage multi-object tracking accuracy (AMOTA) among all camera-based methodson the competitive NuScenes 3D tracking benchmark, outperforming previouslypublished methods by 6.5% in AMOTA with the same 3D detector.

CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion